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        <title>micrograd</title>

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        <h1>Micrograd - A tiny Autograd engine (with a bite! :))</h1>
        <br />

        <py-config>
            packages = [
              "micrograd",
              "numpy",
              "matplotlib"
            ]
        </py-config>

        <div>
            <p>
                <a href="https://github.com/karpathy/micrograd">Micrograd</a> is
                a tiny Autograd engine created by
                <a href="https://twitter.com/karpathy">Andrej Karpathy</a>. This
                app recreates the
                <a
                    href="https://github.com/karpathy/micrograd/blob/master/demo.ipynb"
                    >demo</a
                >
                he prepared for this package using pyscript to train a basic
                model, written in Python, natively in the browser. <br />
            </p>
        </div>
        <div>
            <p>
                You may run each Python REPL cell interactively by pressing
                (Shift + Enter) or (Ctrl + Enter). You can also modify the code
                directly as you wish. If you want to run all the code at once,
                not each cell individually, you may instead click the 'Run All'
                button. Training the model takes between 1-2 min if you decide
                to 'Run All' at once. 'Run All' is your only option if you are
                running this on a mobile device where you cannot press (Shift +
                Enter). After the model is trained, a plot image should be
                displayed depicting the model's ability to classify the data.
                <br />
            </p>
            <p>
                Currently the <code>&gt;</code> symbol is being imported
                incorrectly as <code>&amp;gt;</code> into the REPL's. In this
                app the <code>&gt;</code> symbol has been replaced with
                <code>().__gt__()</code> so you can run the code without issue.
                Ex: instead of <code>a &gt; b</code>, you will see
                <code>(a).__gt__(b)</code> instead. <br />
            </p>
            <py-script>
                import js; js.document.getElementById('python-status').innerHTML = 'Python is now ready. You may proceed.'
            </py-script>
            <div id="python-status">
                Python is currently starting. Please wait...
            </div>
            <button
                id="run-all-button"
                class="btn btn-primary"
                type="submit"
                py-click="run_all_micrograd_demo()"
            >
                Run All</button
            ><br />
            <py-script src="/micrograd_ai.py"></py-script>
            <div id="micrograd-run-all-print-div"></div>
            <br />
            <div id="micrograd-run-all-fig1-div"></div>
            <div id="micrograd-run-all-fig2-div"></div>
            <br />
        </div>
        <py-repl auto-generate="false">
            import random import numpy as np import matplotlib.pyplot as plt </py-repl
        ><br />
        <py-repl auto-generate="false">
            from micrograd.engine import Value from micrograd.nn import Neuron,
            Layer, MLP </py-repl
        ><br />
        <py-repl auto-generate="true">
            np.random.seed(1337) random.seed(1337) </py-repl
        ><br />
        <py-repl auto-generate="true">
            #An adaptation of sklearn's make_moons function
            https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html
            def make_moons(n_samples=100, noise=None): n_samples_out,
            n_samples_in = n_samples, n_samples outer_circ_x =
            np.cos(np.linspace(0, np.pi, n_samples_out)) outer_circ_y =
            np.sin(np.linspace(0, np.pi, n_samples_out)) inner_circ_x = 1 -
            np.cos(np.linspace(0, np.pi, n_samples_in)) inner_circ_y = 1 -
            np.sin(np.linspace(0, np.pi, n_samples_in)) - 0.5 X =
            np.vstack([np.append(outer_circ_x, inner_circ_x),
            np.append(outer_circ_y, inner_circ_y)]).T y =
            np.hstack([np.zeros(n_samples_out, dtype=np.intp),
            np.ones(n_samples_in, dtype=np.intp)]) if noise is not None: X +=
            np.random.normal(loc=0.0, scale=noise, size=X.shape) return X, y X,
            y = make_moons(n_samples=100, noise=0.1) </py-repl
        ><br />
        <py-repl auto-generate="true">
            y = y*2 - 1 # make y be -1 or 1 # visualize in 2D
            plt.figure(figsize=(5,5)) plt.scatter(X[:,0], X[:,1], c=y, s=20,
            cmap='jet') plt </py-repl
        ><br />
        <py-repl auto-generate="true">
            model = MLP(2, [16, 16, 1]) # 2-layer neural network print(model)
            print("number of parameters", len(model.parameters())) </py-repl
        ><br />

        <div>
            Line 24 has been changed from: <br />
            <code
                >accuracy = [(yi &gt; 0) == (scorei.data &gt; 0) for yi, scorei
                in zip(yb, scores)]</code
            ><br />
            to: <br />
            <code
                >accuracy = [((yi).__gt__(0)) == ((scorei.data).__gt__(0)) for
                yi, scorei in zip(yb, scores)]</code
            ><br />
        </div>

        <py-repl auto-generate="true">
            # loss function def loss(batch_size=None): # inline DataLoader :) if
            batch_size is None: Xb, yb = X, y else: ri =
            np.random.permutation(X.shape[0])[:batch_size] Xb, yb = X[ri], y[ri]
            inputs = [list(map(Value, xrow)) for xrow in Xb] # forward the model
            to get scores scores = list(map(model, inputs)) # svm "max-margin"
            loss losses = [(1 + -yi*scorei).relu() for yi, scorei in zip(yb,
            scores)] data_loss = sum(losses) * (1.0 / len(losses)) # L2
            regularization alpha = 1e-4 reg_loss = alpha * sum((p*p for p in
            model.parameters())) total_loss = data_loss + reg_loss # also get
            accuracy accuracy = [((yi).__gt__(0)) == ((scorei.data).__gt__(0))
            for yi, scorei in zip(yb, scores)] return total_loss, sum(accuracy)
            / len(accuracy) total_loss, acc = loss() print(total_loss, acc) </py-repl
        ><br />
        <py-repl auto-generate="true">
            # optimization for k in range(20): #was 100. Accuracy can be further
            improved w/ more epochs (to 100%). # forward total_loss, acc =
            loss() # backward model.zero_grad() total_loss.backward() # update
            (sgd) learning_rate = 1.0 - 0.9*k/100 for p in model.parameters():
            p.data -= learning_rate * p.grad if k % 1 == 0: print(f"step {k}
            loss {total_loss.data}, accuracy {acc*100}%") </py-repl
        ><br />
        <div>
            <p>
                Please wait for the training loop above to complete. It will not
                print out stats until it has completely finished. This typically
                takes 1-2 min. <br /><br />

                Line 9 has been changed from: <br />
                <code>Z = np.array([s.data &gt; 0 for s in scores])</code><br />
                to: <br />
                <code>Z = np.array([(s.data).__gt__(0) for s in scores])</code
                ><br />
            </p>
        </div>
        <py-repl auto-generate="true">
            h = 0.25 x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min,
            y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy =
            np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
            Xmesh = np.c_[xx.ravel(), yy.ravel()] inputs = [list(map(Value,
            xrow)) for xrow in Xmesh] scores = list(map(model, inputs)) Z =
            np.array([(s.data).__gt__(0) for s in scores]) Z =
            Z.reshape(xx.shape) fig = plt.figure() plt.contourf(xx, yy, Z,
            cmap=plt.cm.Spectral, alpha=0.8) plt.scatter(X[:, 0], X[:, 1], c=y,
            s=40, cmap=plt.cm.Spectral) plt.xlim(xx.min(), xx.max())
            plt.ylim(yy.min(), yy.max()) plt </py-repl
        ><br />
        <py-repl auto-generate="true"> 1+1 </py-repl><br />
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